Least square support vector machines in combination with principal component analysis for electronic nose data classification

摘要：

In this paper, an electronic nose data classification approach based on least square support vector machines (LS-SVM) in combination with principal component analysis (PCA) is investigated. The electronic nose data are first converted into PCA, where the data are projected from a high dimensional space into a low dimensional space, preferably two or three dimensions. Then the resulting features from the PCA are sent into the LS-SVM classifier in order to recognize the gas category. The performance of the proposed approach is validated by cross-validation technique. An experiment has been demonstrated by using coffee data from different types of coffee blends. Experimental results show that the LS-SVM in combination with PCA is an effective technique for the classification of electronic nose data.

关键词： least square support vector machines (LS-SVM) principal component analysis (PCA) electronic nose classification

作者: Xiaodong Wang Jianli Chang Ke Wang Meiying Ye

作者单位: Department of Electronic Engineering Zhejiang Normal University Jinhua, China Department of Physics Zhejiang Normal University Jinhua, China

会议类型: 国际会议

会议名称: Second International Symposium on Information Science and Engineering(第二届信息科学与工程国际会议)

会议地点: 上海

会议语种:英文

页码: 348-352

在线出版日期: 2009-12-26（万方平台首次上网日期，不代表论文的发表时间）